Salmonberry and salal annual aerial stem production: the maintenance of shrub cover in forest stands
Bibliographic record
Abstract
Annual sprouting of aerial stems and ramets enables populations of salmonberry (Rubus spectabilis Pursh), salal (Gaultheria shallon Pursh), and probably other forest shrubs to maintain dense covers (>20 000 stems/ha). We studied annual stem production of salmonberry on cut (all stems cut within 15 cm of the ground) and uncut (stems were not treated) plots for 8 years and salal for 5 years in the understories of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), alder, and riparian stands, as well as clearcuts, which are all common stand types in western Oregon. Mean salmonberry stem production on uncut plots ranged from 4.7 stems·m2·year1 (95% CI 2.97.4) in alder stands and clearcuts to 1.6 stems·m2·year1 (95% CI 1.02.6) in conifer stands. Mean salal production was greater, ranging from 58 stems·m2·year1 (95% CI 25135) to 8.6 stems·m2·year1 (95% CI 3.720.1) on uncut plots in clearcuts and unthinned Douglas-fir stands, respectively. Annual production of both species was somewhat greater on cut plots. Most stems produced in early spring die by December, but enough are recruited to replace mortality of older stems. Stem density was maintained for 8 years for salmonberry and 5 years for salal on both cut and uncut plots. Based on length of rhizomes and bud density we estimate that only 15% of the buds in the rhizomes are needed to support this annual stem production. Although these species sprout vigorously after their aerial stems are killed, disturbance is not necessary for maintaining a dense cover. It appears that, once established, salal, salmonberry, and probably other clonal forest shrubs can maintain a dense cover that can interfere with establishment of trees and other shrubs in canopy gaps or other openings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".